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Expanding defense capabilities by applying deep learning techniques

Posted by Vincent Chuffart|
November 17, 2016

Technologies leveraged from advanced computing in data centers are opening up new ways to tackle defense missions across embedded platforms. Deep learning is an increasingly popular approach to processing very large data sets. Many high-visibility projects involved with image processing and data mining use deep learning techniques to predict and evaluate future events and courses of action. The US Department of Homeland Security’s Synthetic Environment for Analysis and Simulations (SEAS) project for example is currently using the SEAS that was developed by Purdue University to predict and evaluate future events and courses of action. While deep learning methodologies are not exactly new, the processing power needed for such complex applications is finally becoming miniaturized and low-power enough for packaging into embedded computing systems.

hpec up for deep learning challenges

Deep learning applications can utilize technologies such as high-speed switched serial links, rugged standardized form factors, and HPEC (High-Performance Embedded Computing) middleware. These technologies have been developed and honed over the years to address HPEC problems such as synthetic aperture radar (SAR) and military signal intelligence (SIGINT) applications. The challenge for the system integrator, therefore, is to define how deep learning algorithms can be applied to solve their particular problem.

Above is an example of how deep learning works on an embedded HPEC system. Requiring a huge amount of computation in the testing phase, a snapshot of the network is taken with each training result and tested. This process is repeated with the expectation that the next snapshot will respond better than the previous one.

As defense system use evolves in providing greater application autonomy, and as deep learning techniques tend to be most useful for pattern recognition tasks such as natural language processing and image feature detection, it makes sense that deep learning could be successfully applied for on-platform processing of streaming signal or image data. These systems would have the power to sift through voluminous streams of data looking for either signals or targets of interest.

The strategic role of deep learning

It is possible to build modular HPEC systems optimized for deep learning applications with readily available platforms based on the Intel® Xeon® Processor D-1540 (Broadwell DE). Stimulated by today’s leading-edge and powerful HPEC platforms such as Kontron’s StarVX, deep learning applications can play a strategic role in advancing future military operations.

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About Kontron

Kontron is a global leader in embedded computing technology (ECT). As a part of technology group S&T, Kontron offers a combined portfolio of secure hardware, middleware and services for Internet of Things (IoT) and Industry 4.0 applications. With its standard products and tailor-made solutions based on highly reliable state-of-the-art embedded technologies, Kontron provides secure and innovative applications for a variety of industries. As a result, customers benefit from accelerated time-to-market, reduced total cost of ownership, product longevity and the best fully integrated applications overall.

Kontron is a listed company. Its shares are traded in the Prime Standard segment of the Frankfurt Stock Exchange and on other exchanges under the symbol "KBC".